EXTENDING GEOSPATIAL REPOSITORIES WITH GEOSEMANTIC PROXIMITY FUNCTIONALITIES TO FACILITATE THE INTEROPERABILITY OF GEOSPATIAL DATA
Bibliographic record
Abstract
Today, with the common availability of Internet technologies, interoperability of geospatial data has become a necessity for sharing and integrating geospatial data. More specifically, it is seen as a solution to solve syntactic, structural, semantic, geometric and temporal heterogeneities between data sources. In Canada, we observe such heterogeneities from existing geospatial databases. For example, Vegetation, Trees, Wooded area, Wooded area, Milieu boisé and Zone boisée (unknown geometry), found in different geospatial data specifications, describe the same type of phenomena. Recently, we have proposed a conceptual framework for geospatial data interoperability based on human communication concepts. This framework introduces the idea of geosemantic proximity, which provides reasoning capabilities to assess the semantic, geometric, and temporal similarities between geospatial concepts and geospatial conceptual representations. In the present paper, we review the conceptual framework and present an architecture of a system based on this framework. In fact, the architecture uses a geospatial repository, namely Perceptory, as a data source’s ontology upon which we add geosemantic proximity functionalities. These functionalities evaluate the similarity of the information stored in the data source with the information required by another one in order to facilitate the interoperability of geospatial data. 1.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".